The end-to-end predictor we used in this work is backboned by the social LSTM model [1]}. Similar to the planning-based predictor, we only consider surrounding agents which are within a distance range of the target agent. All agents' coordinates are centered based on the current (the most recent timestep in the history trajectory) position of the target vehicle. The historical motions of each agent is encoded separately with a long short-term memory (LSTM) module, and pooling layers on the hidden states are performed to encourage interactions among agents, as shown in fig:lstmmodel.
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